[hoiuyoi9087]

Branch heavy code, for example trading order book updating.

People always say "use numpy", but that is only possible if your algorithm can be described in terms of vectorized operations. For many kinds of processing, the only alternative is C/C++ (through Cython)

[steev]

That was my hunch.

> People always say "use numpy", but that is only possible if your algorithm can be described in terms of vectorized operations. For many kinds of processing, the only alternative is C/C++ (through Cython

Agreed

[cf]

I think using numpy is always good first step after just trying to improve the algorithm. Numpy will be less effort than going to cython. After that cython is a good next step. I seriously don't know any situation where I would do the kind of micro-optimizations mentioned in the article.

[Sean1708]

> (through Cython)

My personal experience is that you can actually get another factor of 2 or 3 speed-up by ditching Cython and using actual C instead (I think it's because optimizers have a hard time cleaning up the C that Cython produces), even if you've turned off thing's like bounds checking.

[hoiuyoi9087]

I meant "through Cython" as in "passing through Cython", ie, Cython is just a thin wrapper layer over the real C/C++ code.